Recently, many deep learning-based methods have been proposed to accurately and quickly segment organs from abdominal CT volumes [1]}, [2]}, [3]}. However, these methods just were evaluated on very small datasets or in-house datasets or just segmented very few organs. The main reason caused these problems is that lacking of an open available large-scale real clinical dataset with fine whole abdominal organs annotation. AbdomenCT-1K [2]} collected and annotated more than 1000 abdominal CT scans from existing datasets, each scan has four organs annotation. It further points out that abdominal organ segmentation is a solved problem for liver, spleen, kidney, and pancreas segmentation but the whole abdominal organ segmentation remains an unsolved problem. BTCV [5]} dataset has 50 CT volumes, each volume has 13 organs annotations. DenseVNet [6]} proposed a large dataset that includes 90 CT volumes with 8 organs annotation, where 47 volumes come from the BTCV [5]} dataset and other 43 volumes from
the TCIA [8]} dataset (NIH-Pancreas). Here, we just review several widely-used datasets, some small datasets, just a few organs annotated datasets, or private or not available datasets, which have out of the scope of this work.
